The present invention relates to an airborne system for determining the position of an aerial vehicle, which may be used not only for automatically guiding said vehicle (particularly but not exclusively in the case of a pilotless vehicle) but also for assisting the pilot of a piloted vehicle.
Inertial navigating systems are known using an inertial unit which have the advantages of independence with respect to the environment and discretion, which is particularly appreciable for military missiles intended to penetrate into enemy territory. In these inertial navigating systems, the present position of the vehicle is determined by up-dating a previous position by integration of accelerometric and gyrometric measurements. Thus, they are subject to drifts which must be compensated for so that the present position of the vehicle may be known with sufficient accuracy.
In addition, in such inertial navigating systems, periodic position measurements are made by means of appropriate sensors, for correcting the results from the integration of said accelerometric and gyrometric measurements.
Particularly, when the flight of said vehicle must be independent and discreet, it is advantageous for such periodic resetting measurements to result from correlation between the present image of the ground being flown over delivered by an airborne sensor and a reference image of the ground to be flown over, this reference image being established prior to the flight and stored in a memory provided on board said aerial vehicle. To form the present image and/or the reference image of the ground, it is possible as is known to use numerous types of sensors, such as the altimeter (the image is then formed by the relief of the ground), millimetric radar, millimetric radiometry, infrared or optical imagery systems or else the laser.
Thus, in these known systems, a correlation function is formed between an image taken over a large ground area (the present image) and a similar image learned previously and available in the on-board memory (the reference image), which assumes learning by the machine of the whole of the ground to be flown over for the magnitude used by the sensor.
The result of the correlation between said present image and said reference image forms then a position error signal which, by way of unique innovation may be applied to a Kalman filter integrating the inertial measurements. Thus, periodically, said filter is reset for delivering a precise present position, from which the drifts are eliminated.
Known inertial navigating systems of this type have however drawbacks.
First of all, because of their very structure comprising a Kalman filter, they are adapted to use the present image of only a single sensor; thus, they cannot pertinently take into account several present images coming from as many different sensors, which would however increase the accuracy of the present position by judicious use of the complementarity of certain sensors. In addition, they require the reference image to comprise all the information equivalent to that which said sensor is able to give for the present image, i.e. it is necessary to learn the whole of the ground to be flown over for the magnitude measured by the sensor and for all the observation conditions. Such learning is difficult, if not impossible to acquire in a sufficiently reliable way; in any case, it is tricky and long to work out. The result is moreover that such known systems require on board the vehicle computing means and high capacity memories. Furthermore, the flexibility in use of such known systems is low in so far as the planning or modification of a flight is concerned, since it is dependent on the knowledge and storage of the image of the ground to be flown over.